During the early stages of interface design, designers need to produce multiple sketches to explore a design space. Design tools often fail to support this critical stage, because they insist on specifying more details than necessary. Although recent advances in generative AI have raised hopes of solving this issue, in practice they fail because expressing loose ideas in a prompt is impractical. In this paper, we propose a diffusion-based approach to the low-effort generation of interface sketches. It breaks new ground by allowing flexible control of the generation process via three types of inputs: A) prompts, B) wireframes, and C) visual flows. The designer can provide any combination of these as input at any level of detail, and will get a diverse gallery of low-fidelity solutions in response. The unique benefit is that large design spaces can be explored rapidly with very little effort in input-specification. We present qualitative results for various combinations of input specifications. Additionally, we demonstrate that our model aligns more accurately with these specifications than other models.

翻译：在界面设计的早期阶段，设计师需要生成多份草图以探索设计空间。由于现有设计工具要求指定过多细节，往往难以支持这一关键阶段。尽管生成式AI的最新进展为解决该问题带来了希望，但在实际应用中，由于无法通过提示词有效表达松散的设计构想，这些方法仍存在局限性。本文提出一种基于扩散模型的界面草图低投入生成方法。该方法通过三类输入实现对生成过程的灵活控制，实现了突破性创新：A）文本提示、B）线框图和C）视觉流。设计师可任意组合这些输入，并指定任意细节层级，系统将据此生成多样化的低保真解决方案。其独特优势在于，用户仅需极少的输入规范投入即可快速探索大规模设计空间。我们展示了不同输入组合下的定性实验结果，同时证明本模型比现有其他模型能更精准地匹配用户输入规范。